SoroushVahidi/scidocs huggingface dataset:
Tomographic reconstruction of piecewise smooth images
(2004)

In computed tomography, direct inversion of the Radon transform requires more projections than are practical due to constraints in scan time and image accessibility. Therefore, it is necessary to consider the estimation of reconstructed images when the problem is under-constrained, i.e., when a unique solution does not exist. To resolve ambiguities among solutions, it is necessary to place additional constraints on the reconstructed image. In this paper, we present a surface evolution technique to model the reconstructed image as piecewise smooth. We model the reconstructed image as two regions that are each smoothly varying in intensity and are separated by a smooth surface. We define a cost functional to penalize deviation from piecewise smoothness while ensuring that the projections of the estimated image match the measured projections. From this functional, we derive an evolution for the modeled image intensity and an evolution for the surface, thereby defining a variational tomographic estimation technique. We show example reconstructions to highlight the performance of the proposed method on real medical images.
Clustering and Summarizing Protein-Protein Interaction Networks: A Survey
(2016)

The increasing availability and significance of large-scale protein-protein interaction (PPI) data has resulted in a flurry of research activity to comprehend the organization, processes, and functioning of cells by analyzing these data at network level. Network clustering, that analyzes the topological and functional properties of a PPI network to identify clusters of interacting proteins, has gained significant popularity in the bioinformatics as well as data mining research communities. Many studies since the last decade have shown that clustering PPI networks is an effective approach for identifying functional modules, revealing functions of unknown proteins, etc. In this paper, we examine this issue by classifying, discussing, and comparing a wide ranging approaches proposed by the bioinformatics community to cluster PPI networks. A pervasive desire of this review is to emphasize the uniqueness of the network clustering problem in the context of PPI networks and highlight why generic network clustering algorithms proposed by the data mining community cannot be directly adopted to address this problem effectively. We also review a closely related problem to PPI network clustering, network summarization, which can enable us to make sense out of the information contained in large PPI networks by generating multi-level functional summaries.
Find me if you can: improving geographical prediction with social and spatial proximity
(2010)

Geography and social relationships are inextricably intertwined; the people we interact with on a daily basis almost always live near us. As people spend more time online, data regarding these two dimensions -- geography and social relationships -- are becoming increasingly precise, allowing us to build reliable models to describe their interaction. These models have important implications in the design of location-based services, security intrusion detection, and social media supporting local communities.
 Using user-supplied address data and the network of associations between members of the Facebook social network, we can directly observe and measure the relationship between geography and friendship. Using these measurements, we introduce an algorithm that predicts the location of an individual from a sparse set of located users with performance that exceeds IP-based geolocation. This algorithm is efficient and scalable, and could be run on a network containing hundreds of millions of users.
Syntax-Tree Regular Expression Based DFA FormalConstruction
(2012)

Compiler is a program whose functionality is to translate a computer program written in source language into an equivalent machine code. Compiler construction is an advanced research area because of its size and complexity. The source codes are in higher level languages which are usually complex and, consequently, increase the level of abstraction. Due to such reasons, design and construction of error free compiler is a challenge of the twenty first century. Verification of a source program does not guarantee about correctness of code generated because the bugs in compiler may lead to an incorrect target program. Therefore, verification of compiler is more important than verifying the source programs. Lexical analyzer is a main phase of compiler used for scanning input and grouping into sequence of tokens. In this paper, formal construction of deterministic finite automata (DFA) based on regular expression is presented as a part of lexical analyzer. At first, syntax tree is described based on the augmented regular expression. Then formal description of important operators, checking null-ability and computing first and last positions of internal nodes of the tree is described. In next, the transition diagram is described from the follow positions and converted into deterministic finite automata by defining a relationship among syntax tree, transition diagram and DFA. Formal specification of the procedure is described using Z notation and model analysis is provided using Z/Eves toolset.
Supervised Dictionary Learning by a Variational Bayesian Group Sparse Nonnegative Matrix Factorization
(2014)

Nonnegative matrix factorization (NMF) with group sparsity constraints is formulated as a probabilistic graphical model and, assuming some observed data have been generated by the model, a feasible variational Bayesian algorithm is derived for learning model parameters. When used in a supervised learning scenario, NMF is most often utilized as an unsupervised feature extractor followed by classification in the obtained feature subspace. Having mapped the class labels to a more general concept of groups which underlie sparsity of the coefficients, what the proposed group sparse NMF model allows is incorporating class label information to find low dimensional label-driven dictionaries which not only aim to represent the data faithfully, but are also suitable for class discrimination. Experiments performed in face recognition and facial expression recognition domains point to advantages of classification in such label-driven feature subspaces over classification in feature subspaces obtained in an unsupervised manner.
Acute Sexual Assault in the Pediatric and Adolescent Population.
(2016)

Children and adolescents are at high risk for sexual assault. Early medical and mental health evaluation by professionals with advanced training in sexual victimization is imperative to assure appropriate assessment, forensic evidence collection, and follow-up. Moreover, continued research and outreach programs are needed for the development of preventative strategies that focus on this vulnerable population. In this review we highlight key concepts for assessment and include a discussion of risk factors, disclosure, sequelae, follow-up, and prevention.
Listening Comprehension over Argumentative Content
(2018)

This paper presents a task for machine listening comprehension in the argumentation domain and a corresponding dataset in English. We recorded 200 spontaneous speeches arguing for or against 50 controversial topics. For each speech, we formulated a question, aimed at confirming or rejecting the occurrence of potential arguments in the speech. Labels were collected by listening to the speech and marking which arguments were mentioned by the speaker. We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset. All data used in this work is freely available for research.
MauveDB: supporting model-based user views in database systems
(2006)

Real-world data --- especially when generated by distributed measurement infrastructures such as sensor networks --- tends to be incomplete, imprecise, and erroneous, making it impossible to present it to users or feed it directly into applications. The traditional approach to dealing with this problem is to first process the data using statistical or probabilistic models that can provide more robust interpretations of the data. Current database systems, however, do not provide adequate support for applying models to such data, especially when those models need to be frequently updated as new data arrives in the system. Hence, most scientists and engineers who depend on models for managing their data do not use database systems for archival or querying at all; at best, databases serve as a persistent raw data store.In this paper we define a new abstraction called model-based views and present the architecture of MauveDB, the system we are building to support such views. Just as traditional database views provide logical data independence, model-based views provide independence from the details of the underlying data generating mechanism and hide the irregularities of the data by using models to present a consistent view to the users. MauveDB supports a declarative language for defining model-based views, allows declarative querying over such views using SQL, and supports several different materialization strategies and techniques to efficiently maintain them in the face of frequent updates. We have implemented a prototype system that currently supports views based on regression and interpolation, using the Apache Derby open source DBMS, and we present results that show the utility and performance benefits that can be obtained by supporting several different types of model-based views in a database system.
Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora
(1997)

We introduce (1) a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence-pairs, and (2) the concept of bilingual parsing with a variety of parallel corpus analysis applications. Aside from the bilingual orientation, three major features distinguish the formalism from the finite-state transducers more traditionally found in computational linguistics: it skips directly to a context-free rather than finite-state base, it permits a minimal extra degree of ordering flexibility, and its probabilistic formulation admits an efficient maximum-likelihood bilingual parsing algorithm. A convenient normal form is shown to exist. Analysis of the formalism's expressiveness suggests that it is particularly well suited to modeling ordering shifts between languages, balancing needed flexibility against complexity constraints. We discuss a number of examples of how stochastic inversion transduction grammars bring bilingual constraints to bear upon problematic corpus analysis tasks such as segmentation, bracketing, phrasal alignment, and parsing.
Feature Detection with Automatic Scale Selection
(1998)

The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works, Witkin (1983) and Koenderink (1984) proposed to approach this problem by representing image structures at different scales in a so-called scale-space representation. Traditional scale-space theory building on this work, however, does not address the problem of how to select local appropriate scales for further analysis. This article proposes a systematic methodology for dealing with this problem. A framework is presented for generating hypotheses about interesting scale levels in image data, based on a general principle stating that local extrema over scales of different combinations of γ-normalized derivatives are likely candidates to correspond to interesting structures. Specifically, it is shown how this idea can be used as a major mechanism in algorithms for automatic scale selection, which adapt the local scales of processing to the local image structure. Support for the proposed approach is given in terms of a general theoretical investigation of the behaviour of the scale selection method under rescalings of the input pattern and by integration with different types of early visual modules, including experiments on real-world and synthetic data. Support is also given by a detailed analysis of how different types of feature detectors perform when integrated with a scale selection mechanism and then applied to characteristic model patterns. Specifically, it is described in detail how the proposed methodology applies to the problems of blob detection, junction detection, edge detection, ridge detection and local frequency estimation. In many computer vision applications, the poor performance of the low-level vision modules constitutes a major bottleneck. It is argued that the inclusion of mechanisms for automatic scale selection is essential if we are to construct vision systems to automatically analyse complex unknown environments.
A Combined Corner and Edge Detector
(1988)

The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work. For example, we desire to obtain an understanding of natural scenes, containing roads, buildings, trees, bushes, etc., as typified by the two frames from a sequence illustrated in Figure 1. The solution to this problem that we are pursuing is to use a computer vision system based upon motion analysis of a monocular image sequence from a mobile camera. By extraction and tracking of image features, representations of the 3D analogues of these features can be constructed.
Singularity detection and processing with wavelets
(1992)

Most of a signal information is often carried by irregular structures and transient phenomena. The mathematical characterization of singularities with Lipschitz exponents is explained. Theorems are reviewed that estimate local Lipschitz exponents of functions from the evolution across scales of their wavelet transform. It is then proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents. The wavelet transform of singularities with fast oscillations have a particular behavior that is studied separately. The local frequency of such oscillations are measured from the wavelet transform modulus maxima. It has been shown numerically that oneand two-dimensional signals can be reconstructed, with a good approximation, from the local maxima of their wavelet transform modulus. As an application, an algorithm is developed that removes white noises from signals by analyzing the evolution of the wavelet transform maxima across scales. In two-dimensions, the wavelet transform maxima indicate the location of edges in images. The denoising algorithm is extended for image enhancement.
Modeling Visual Attention via Selective Tuning
(1995)

A model for aspects of visual attention based on the concept of selective tuning is presented. It provides for a solution to the problems of selection in an image, information routing through the visual processing hierarchy and task-specific attentional bias. The central thesis is that attention acts to optimize the search procedure inherent in a solution to vision. It does so by selectively tuning the visual processing network which is accomplished by a top-down hierarchy of winner-take-all processes embedded within the visual processing pyramid. Comparisons to other major computational models of attention and to the relevant neurobiology are included in detail throughout the paper. The model has been implemented; several examples of its performance are shown. This model is a hypothesis for primate visual attention, but it also outperforms existing computational solutions for attention in machine vision and is highly appropriate to solving the problem in a robot vision system.
SIDES: a cooperative tabletop computer game for social skills development
(2006)

This paper presents a design case study of SIDES: Shared Interfaces to Develop Effective Social Skills. SIDES is a tool designed to help adolescents with Asperger's Syndrome practice effective group work skills using a four-player cooperative computer game that runs on tabletop technology. We present the design process and evaluation of SIDES conducted over six months with a middle school social group therapy class. Our findings indicate that cooperative tabletop computer games are a motivating and supportive tool for facilitating effective group work among our target population and reveal several design lessons to inform the development of similar systems.
On Controllable Sparse Alternatives to Softmax
(2018)

Converting an n-dimensional vector to a probability distribution over n objects is a commonly used component in many machine learning tasks like multiclass classification, multilabel classification, attention mechanisms etc. For this, several probability mapping functions have been proposed and employed in literature such as softmax, sum-normalization, spherical softmax, and sparsemax, but there is very little understanding in terms how they relate with each other. Further, none of the above formulations offer an explicit control over the degree of sparsity. To address this, we develop a unified framework that encompasses all these formulations as special cases. This framework ensures simple closed-form solutions and existence of sub-gradients suitable for learning via backpropagation. Within this framework, we propose two novel sparse formulations, sparsegen-lin and sparsehourglass, that seek to provide a control over the degree of desired sparsity. We further develop novel convex loss functions that help induce the behavior of aforementioned formulations in the multilabel classification setting, showing improved performance. We also demonstrate empirically that the proposed formulations, when used to compute attention weights, achieve better or comparable performance on standard seq2seq tasks like neural machine translation and abstractive summarization.
S-CNN: Subcategory-Aware Convolutional Networks for Object Detection
(2018)

The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.
Revealing protein–lncRNA interaction
(2016)

Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein-RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP-lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein-lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations.
gHull: A GPU algorithm for 3D convex hull
(2013)

A novel algorithm is presented to compute the convex hull of a point set in ℝ3 using the graphics processing unit (GPU). By exploiting the relationship between the Voronoi diagram and the convex hull, the algorithm derives the approximation of the convex hull from the former. The other extreme vertices of the convex hull are then found by using a two-round checking in the digital and the continuous space successively. The algorithm does not need explicit locking or any other concurrency control mechanism, thus it can maximize the parallelism available on the modern GPU.
 The implementation using the CUDA programming model on NVIDIA GPUs is exact and efficient. The experiments show that it is up to an order of magnitude faster than other sequential convex hull implementations running on the CPU for inputs of millions of points. The works demonstrate that the GPU can be used to solve nontrivial computational geometry problems with significant performance benefit.
gTRICLUSTER: A More General and Effective 3D Clustering Algorithm for Gene-Sample-Time Microarray Data
(2006)

Clustering is an important technique in microarray data analysis, and mining three-dimensional (3D) clusters in gene-sample-time (simply GST) microarray data is emerging as a hot research topic in this area. A 3D cluster consists of a subset of genes that are coherent on a subset of samples along a segment of time series. This kind of coherent clusters may contain information for the users to identify useful phenotypes, potential genes related to these phenotypes and their expression rules. TRICLUSTER is the state-of-the-art 3D clustering algorithm for GST microarray data. In this paper, we propose a new algorithm to mine 3D clusters over GST microarray data. We term the new algorithm gTRICLUSTER because it is based on a more general 3D cluster model than the one that TRICLUSTER is based on. gTRICLUSTER can find more biologically meaningful coherent gene clusters than TRICLUSTER can do. It also outperforms TRICLUSTER in robustness to noise. Experimental results on a real-world microarray dataset validate the effectiveness of the proposed new algorithm.